Researchers and Policymakers Point to Successes and Challenges in Personalized Medicine

As lawmakers in Congress debated healthcare reform, National Institutes of Health Director Francis Collins and other top thinkers suggested at AAAS that personalized medicine and other future health strategies could someday improve care and bring down medical costs.

Current forecasts find that health costs are rising so fast, Collins said, that medical care may account for 20% of U.S. gross domestic product by 2020. But if personal medicine builds on its early advances in analyzing and treating individuals’ ills at the genetic level, and if other novel strategies improve understanding of the most effective treatments, health care could evolve dramatically to ease the pressure on costs.

Peter C. Agre and Francis Collins

Collins offered an example of how personalized medicine is already yielding better diagnostic treatments: The OncotypeDx breast cancer test identifies gene expression in tumors that predict whether the patient will respond to chemotherapy. “In the past five years,” he said, “this has been more and more adopted by oncologists and patients.” When the test demonstrates that patients will not respond to chemotherapy, the patients avoid an average $2000 in chemotherapy costs, or a total of $100 million nationwide this year.

The two-day colloquium on personalized medicine, held 26-27 October, was organized by the AAAS Scientific Freedom, Responsibility and Law Program and the Food and Drug Law Institute. It was the second of three planned colloquia on personalized medicine, and over 200 participants attended or watched a webcast of the event. The third in the series will be 8-9 March in Phoenix, Arizona.

The most recent event offered keen insight into a range of issues that will shape health care in the 21st century: genetic medicine; comparative effectiveness research, which assesses patient outcomes to determine the best care strategies; and the regulations that will shape this new age of medicine.

Margaret Hamburg

Collins, former director of the National Human Genome Research Institute at the NIH, and Margaret Hamburg, newly appointed commissioner of the U.S. Food and Drug Administration, each suggested ways their agencies could help the nation move more quickly from basic research to FDA-approved therapeutics.

Clearly, the speakers said, the potential of personalized medicine is far from being fulfilled. But there’s been steady progress.

“Small, but significant, advances have been made related to prescription drug dosing that have already reduced risks of adverse reaction or have led to more appropriate treatment,” said Mark Frankel, director of the AAAS Scientific Freedom, Responsibility and Law Program. “Nevertheless, there is still much to be learned and many years to go before personalized medicine becomes routine medical practice.”

Frankel said that personalized medicine continues to face challenges and gaps in basic science, the country’s regulatory system for approval of drugs and devices, and insurance reimbursement policies. “None of the new medical data will matter much, however, unless health care providers are willing and able to use all this new knowledge with confidence,” Frankel said.

Teasing Out Heritability in Disease

Having your genome scanned for possible disease-associated genetic variants is becoming more and more accessible. And as parents become increasingly interested in carrier screening before conception as well as comprehensive newborn screening for inheritable diseases, Collins said, “whether you like it or not, a complete sequencing of newborns is not far away.”

Genotyping, as it is known, is offered by companies to consumers interested in heritable disease. Collins already has had his own genome analyzed.

When the human genome was sequenced earlier this decade, many saw it as the key to understanding who becomes sick and why, and as a way to deliver more effective treatments. But as we approach the end of the decade, genetic understanding of disease has fallen short.

Collins described how genome-wide association studies, in which genomes of large groups of people are scanned and compared, have revealed “utterly surprising” genetic contributors to diseases. Rather than have genetic variants that turn a disease on or off, the genetic reasons for getting a disease are far more subtle. Collins explained how many genome studies reveal that as opposed to an “off/on” switch, disease-associated genetic variants have more to do with when or how long a gene is activated. Those findings make genetic understanding of disease more nuanced, and present “quite a challenge to the science community,” Collins said.

But genetics only account for a portion of why and how people get sick. A surprising and perhaps disappointing finding, Collins said, is that despite all the disease-associated genetic variance that has been identified, it only accounts for less than 10% of heritability of disease. That is, even if you have a disease-associated genetic variant, you might not actually get the disease. “There’s a lot of missing variability that hasn’t popped up,” Collins said.

Where’s the missing variability, the so-called dark matter of the genome? It could come from environmental factors, low-frequency common variants or other unidentified factors. The “greatest opportunity” for finding the missing variability may be in identifying the less common variants that could have a significant influence in disease, Collins said.

Personalized medicine might have been the main theme of the AAAS colloquium, but a related and timely healthcare topic was also present: comparative effectiveness research. Collins used part of his talk to address some concerns that have been raised about personalized medicine colliding with comparative effectiveness research.

Whereas “personalized medicine” assesses the person’s genetic makeup, family history, and other individualized characteristics in order to assess which medical treatment might be most effective, comparative effectiveness research—recipient of $1.1 billion through the U.S. economic stimulus package—is more about the treatment. Individuals are randomly assigned to different treatments, and then the treatments are compared for their benefits and harms.

For instance, diet and exercise were more effective than medical therapy in preventing the onset of diabetes in individuals who already had impaired glucose tolerance, according to an NIH study. “You have a sense of what works and are trying to decide which thing works better,” Collins explained.

Is there a collision between comparative effectiveness research and personalized medicine? Collins doesn’t think so. He said that personalized medicine should include genotyping to look for subsets of individuals who may not respond to a therapy or who will have side effects from a therapy. The approach could lead to better diagnostics and preventive strategies, such as which patients under which circumstances will fare best according to which treatment.

“We can’t lose the personalization,” Collins said.

Harold Sox

Harold Sox, co-chair of the Institute of Medicine’s 2009 report on comparative effectiveness research, said that comparative effectiveness research could curb healthcare costs if it enabled doctors and their patients to decide when tests and treatments were likely to improve health outcomes, while avoiding their use when tests and treatments are unlikely to help. In his talk at the AAAS colloquium, Sox described how comparative effectiveness research could help inform in which instances diagnostic tests should be used.

For example, a doctor finds a firm area in a patient who had surgery to remove colon cancer. Should the doctor do a PET scan to see if the area is scar tissue or the tumor growing back? And then do a biopsy only if the PET scan indicates that it’s cancer? Or should the doctor just jump ahead to the biopsy? In this case, Sox said, a PET scan should not be done, because even after the PET scan indicated scar tissue the probability of recurrent cancer was so high that most doctors would do the biopsy. Such a use of comparing different courses of treatment—the hallmark of comparative effectiveness research—could help curb health care costs while helping patients to avoid situations in which they are exposed to the potential harms of tests or treatments that have little chance of benefiting them.

Although Sox also cited recent evidence of how genetic tests can help in some instances, they can add little information in other instances. For example, in predicting the onset of diabetes, genetic tests appear to add little information to that gained from talking to the patient, checking blood pressure and body weight, and doing several other routine screening tests.

Regulatory Science: Safely Going from Bench to Bedside

Regulatory science might not be a subject that interests most of the population, but it certainly has the attention of Margaret Hamburg. As the 21st commissioner of the U.S. Food and Drug Administration, Hamburg described herself as a “zealot” for advancing regulatory science, which includes how methods to evaluate the safety and usefulness of medical products.

Biomedical discoveries must be paired with robust regulatory science, otherwise the nation’s investment in research won’t pay off, Hamburg said at her 26 October talk at AAAS. “Our regulatory scientists must be able to understand therapies that are being developed using the most recent scientific advances. They must have the tools to evaluate these therapies,” said Hamburg, a medical doctor who has conducted research in neuroscience and on AIDS.

Hamburg praised traditional approaches of drug development, such as randomized controlled clinical trials which moved the field of medicine “from anecdote to evidence.” Randomized, controlled clinical trials have increased longevity, Hamburg said, and they have “transformed debilitating diseases like diabetes, AIDS, and rheumatoid arthritis into chronic yet manageable conditions.”

But clinical trials could be improved. Hamburg, previously a public health official in New York City, said that trials need to show not only which drug therapy works, but also the underlying biology of why a drug works and in whom it works. “Clearly we have much better outcomes if we can discern what distinguishes one group from another in terms of positive response and design clinical trials based on that knowledge,” she said.

Such knowledge will help identify at-risk populations of patients whose genetic make up might predispose them to side-effects of drugs that are otherwise effective in other patient populations. For example, a genetic test might indicate that the patient would not respond to a particular drug therapy. Then, a different course of treatment could be followed rather than have the patient unknowingly undergo an ineffective treatment.

“This is important, because when patients are battling cancer they don’t have the time for trial and error,” Hamburg said. “Prescribing an ineffective course of treatment wastes precious time to fight against a potentially fatal disease progression. Furthermore, the toxicity from the exposure to an ineffective therapy can undermine their efforts.” Such a strategy is already used in colon cancer treatment

So what is the future of personalized medicine at the FDA? Hamburg said that the agency needs to take a more holistic approach, including collaboration with academia, other government agencies, and industry. She wants to make FDA policies more transparent for drug-developers, such as by releasing guidelines that industry can use when developing therapeutics. For example, biomarkers, such as a genetic variant, can be used to identify how a patient will respond to a treatment. A draft guideline on the use of biomarker qualifications will likely be released by the FDA by the end of the year.

“This will enable developers to gain a clear picture of the criteria that FDA will use to vet the usefulness of biomarkers in the evaluation of clinical trial data,” Hamburg said.

Case Studies in Personalized Medicine

A lung cancer patient—a non-smoking woman in her mid-50’s from upstate New York—had tried three different forms of chemotherapy and was on supplemental oxygen when her oncologist recommended hospice services. But then doctors tried a new drug, which had a dramatic response. She called the doctor’s office after three days of treatment to say that she “felt like a new woman” and was off of the supplemental oxygen. Within five days of treatment, her chest x-rays showed that the “white fluffy stuff”—signals of cancer—had cleared from her lungs.

What could have caused this patient to respond dramatically to one treatment while showing no response to others? Could biomarkers have predicted the drugs’ effectiveness? Speakers in a panel on case studies in clinical practice at the AAAS personalized medicine colloquium shared some examples of how biomarkers could save time and money.

“I’m a great believer in looking at the data,” said Science Translational Medicine editor Katrina Kelner in introducing the speakers. “It’s interesting to see how some of these issues [of personalized medicine] play out in the real world.”

William Pao

William Pao, assistant director of Vanderbilt University’s Personalized Cancer Medicine Initiative, shared the anecdote of the 45-year old non-smoker who responded so positively to a lung cancer drug, gefitinib. He explained how genotyping could predict effectiveness of lung cancer chemotherapy—a treatment path that has reached a “therapeutic plateau” where survival statistics are “pretty poor,” he said. Showing various chemotherapy regimens, Pao noted that only about 20% of lung cancer patients respond to standard chemotherapy involving a combination of two drugs and that average survival was eight months.

What are the genetic predictors of who will respond? Researchers found a mutation in a gene coding for the receptor on which the drug works to block cancer growth. They hypothesized that patients with those mutations show greater sensitivity to gefitinib. Sure enough, a recent study showed that gefitinib worked in more than 70% of patients whose lung cancer genotype showed the mutation, compared to 1% of patients who lacked the mutation.

Alan Shuldiner

Patients also show varied responses to coronary drugs. Alan Shuldiner, a professor at the University of Maryland School of Medicine, said that there was great variation in patients’ response to clopidogrel (also known as Plavix). Between 4% and 20% of patients receiving the anti-platelet drug do not respond to it. In a genome-wide association study examining response to clopidogrel, Shuldiner and his research team identified a common mutation that was associated with decreased responsiveness to the drug. About one-third of the general population carry this mutation, which is in the cytochrome P450 2C19 (CYP2C19) gene.

The researchers hope to use the findings to develop diagnostic tests to predict response to clopidogrel, ranked the world’s second highest-selling drug in 2005 with $5.9 billion in sales in the United States.

Steven Averbuch

Some have argued that pharmaceutical companies would not want tests that predict the effectiveness of their drugs in certain patients and that such tests would limit their use. But at the AAAS colloquium, Steven Averbuch—a medical oncologist and the head of pharmacodiagnostics at Bristol-Myers Squibb Company—offered a different point of view. Pharmaceutical companies are “vitally interested in giving the right drug to the right patient,” Averbuch said, “because the increased value to the patient and to the physicians caring for the patients should link to the increase [use] of the drug.”

He used the example of the drug abacavir, FDA-approved in 1998 and used to treat HIV and AIDS. About 2-9% of patients can be hypersensitive to abacavir, showing fever, rash and other side effect symptoms. A genetic mutation responsible for hypersensitivity to abacavir was discovered in 2001, and a few years later a skin patch test was released that could predict patients’ level of sensitivity to the drug.

After the diagnostic test was released, there was a “rapid uptake of the drug,” Averbuch said. Within a short time, abacavir drug sales increased by $30 million. “I think the most plausible explanation is that now physicians weren’t avoiding this drug,” he said. “They were giving the right drug to the right patient.”